Faculty Perception of Openness and Attitude to Open Sharing at the Indian National Open University
Bibliographic record
Abstract
In the past decade, the educational scenario world over has significantly been impacted by open access and open education movements. The philosophy of openness and sharing forms the cornerstone of the open education movement. The distance education approaches, together with open educational resources (OER) and massive open online courses (MOOCs), are being used to serve the increasing educational needs of diverse communities. However, adoption of openness as a core value and as part of the institutional strategy still remains a challenge for academic institutions in general, and distance education institutions in particular, in developing countries like India. In this research study, the authors report an analysis of the perception of the faculty of the Indira Gandhi National Open University of India (IGNOU) about openness and their attitude towards sharing of resources in academic institutions. Data was collected through a structured questionnaire administered to the teachers and academics of IGNOU (N=69). The results indicated that: the faculty members valued sharing of resources in academic institutions; learning resources should be made available free of cost; there is a strong need for training on intellectual property rights, copyright, and creation and use of OER; and there should be an institutional policy on OER for its effective use.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".